Automated quality control of structural brain MRI scans from aging and dementia datasets

AbstractBackgroundDementias platform UK (DPUK) currently hosts over 40 dementia cohorts and provides a remote analysis environment for multimodal cross‐cohort data analysis. Nineteen cohorts include brain MRI data, hence automated quality control (QC) becomes crucial for the reliability of image analyses. This work aims to provide a framework for quality prediction of T1‐weighted (T1w) scans from aging and dementia datasets: we compared manual quality ratings with the output of two existing automated QC pipelines and propose a new QC classifier.MethodWe used 2438 T1w scans (including patients and controls, 1.5T and 3T scanners, 3 manufacturers, 11 sites) from 4 different aging and dementia datasets (ADNI, Whitehall II, Oxford Parkinson’s Disease Centre, Oxford Brain Health Clinic). Manual quality ratings for all images were performed by the dataset owners. Each image was processed in MRIQC (Esteban et al. 2017, PLoS ONE) and CAT12 (Computational Anatomy Toolbox, Gaser et al. 2022, bioRxiv). We compared the quality agreement for MRIQC predictions and CAT12’s weighted image quality ratings (IQR) with manual QC using inter‐rater reliability. Further, we explored the effect of changing the accept‐reject threshold from automated QC tools on the reliability measure. Finally, we designed a custom QC classifier (manual QC as target) by combining MRIQC and CAT12’s quality metrics as features and training linear support vector machines in a nested cross‐validation framework.ResultWe found overall agreement between automated QC methods and manual QC when applied to dementia datasets (MRIQC: Kappa = 0.30, p = 0.001; CAT12: Kappa = 0.28, p = 0.001). Adjusting the acceptance thresholds of the automated tools within dataset improved the agreement. On the combined data (training N = 1951; test N = 487), the proposed classifier showed 94.2% accuracy (sensitivity 94.3%, specificity 85.7%) on the test data. Leaving one site out (training with 10 sites, N = 2055), we found 86.4% accuracy (sensitivity 86.2%, specificity 100%) on the test site (N = 383).ConclusionThe performance of the proposed classifier appears promising on these heterogeneous datasets from different scanners. In the future, we aim to utilise this framework for improving generalisability of prediction and release the classifier on the DPUK data portal to robustly QC T1w scans for other aging and dementia cohorts.